Method and system for analyzing blood flow condition

ABSTRACT

The present application relates to a method and system for analyzing blood flow conditions. The method includes: obtaining images at multiple time phases; constructing multiple vascular models corresponding to the multiple time phases; correlating the multiple vascular models; setting boundary conditions of the multiple vascular models respectively based on the result of correlation; and determining condition of blood vessel of the vascular models.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/498,428 filed Apr. 26, 2017, which in turn is a continuation ofInternational Application No. PCT/CN2017/072256 filed Jan. 23, 2017, theentire contents of each of which are hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to a method and system foranalyzing a blood flow condition, and more particularly, to a method andsystem for obtaining multi-time phase blood flow parameters by employinga method of computer fluid dynamics (CFD).

BACKGROUND

CTA and MRA imaging technologies have been widely used in the diagnosesof peripheral vascular diseases, and more particularly, in the diagnosesof the vascular diseases such as vascular stenosis (vertebral arterystenosis), aneurysm, dissecting aneurysm, tumors, tumor-feeding artery,etc. The vascular analysis application provides a tool for vascularanalysis in precise extraction of fine four grade blood vessels,complete bonelessness, fast automatic measurement, etc. In the analysisof blood vessels, medical image analysis systems generally employ animage segmentation technology and an image display technology for a 3-dsimulation reconstruction of the blood vessels of an object. Doctors mayanalyze and process the lesions based on vascular morphological index(e.g., a vascular stenosis degree, a hemangioma expansion degree, etc.).However, the morphological index that may be used are not sufficient.

Computational Fluid Dynamics (CFD) is an interdisciplinary methodrelating to mathematics, fluid mechanics, and computer science. CFD isformed along with the development of computers since the 1950s. The mainresearch target of CFD are simulating and analyzing fluid mechanicsproblems by solving control equations of fluid mechanics with computersand numerical methods. The vascular model or blood flow model is anemerging application that employs computational fluid dynamics.Analyzing a single data using computational fluid mechanics cannotcomprehensively reflect the actual condition and the changing rule ofthe analysis region. Also, selecting the time phase inaccurately maylead to a result of deviation.

SUMMARY

In one aspect of the present disclosure, a method implemented on atleast one device including a processor and a storage is provided. Themethod may include: obtaining a first image related to a first timephase and a second image related to at a second time phase; selecting afirst vascular region from the first image, wherein the first vascularregion includes a blood vessel; selecting a second vascular region fromthe second image, wherein the second vascular region includes at least apart of the blood vessel; generating a first vascular model, wherein thefirst vascular model corresponds to the first vascular region;generating a second vascular model, wherein the second vascular modelcorresponds to the second vascular region; setting a boundary conditionof the first vascular model and a boundary condition of the secondvascular model; determining a condition of the blood vessel of the firstvascular model at the first time phase according to the boundarycondition of the first vascular model; correlating the first vascularmodel and the second vascular model based on the condition of the bloodvessel at the first time phase; and determining a condition of the bloodvessel of the second vascular model at the second time phase accordingto the result of correlation and the boundary condition of the secondvascular model. In some embodiments, “a blood vessel” may refer to ablood vessel or a part thereof. For example, the blood vessel mayinclude an entire coronary artery, a branch of the coronary artery, anentrance cross-section of the coronary artery, etc.

In some embodiments, the correlation of the models corresponding todifferent time phases may include registering the characteristicregions.

In some embodiments, the first vascular region and the second vascularregion may include a coronary artery, an abdominal artery, a cerebralartery, or a lower extremity artery.

In some embodiments, the correlating the first vascular model and thesecond vascular model may include correlating entrances, bifurcationsegments, stenosis segments, or exits of the blood vessel of the firstvascular model and the second vascular model.

In some embodiments, the method may further include generating gridscorresponding to the first vascular model or the second vascular model.

In some embodiments, the generating grids may include: generating2-dimensional grids corresponding to the entrance and the exit of thefirst vascular model; forming grids corresponding to the side wall ofthe first vascular model; and generating, based on the gridscorresponding to the entrance, the exit, and the side wall,3-dimensional grids corresponding to the first vascular model.

In some embodiments, the generating grids may include: generating2-dimensional grids corresponding to the entrance and the exit of thesecond vascular model; forming grids corresponding to the side wall ofthe second vascular model; and generating, based on the gridcorresponding to the entrance, the exit, and the side wall,3-dimensional grids corresponding to the second vascular model.

In some embodiments, the correlating the first vascular model and thesecond vascular model may include matching the grids corresponding tothe first vascular model with the grids corresponding to the secondvascular model.

In some embodiments, the condition of the blood vessel may include:blood velocity, blood pressure, wall stress of the blood vessel, wallshear stress (WSS) of the blood vessel, or fractional flow reserve(FFR).

In some embodiments, the boundary condition of the first vascular modelmay include: determining that the first vascular model is abnormal; inresponse to the determination that the first vascular model is abnormal,determining an abnormal region; generating a normal model correspondingto the first vascular model; obtaining a boundary condition of thenormal model; and generating, based on the boundary condition of thenormal model, a boundary condition corresponding to the first vascularmodel.

In some embodiments, the abnormal vascular model may include vascularstenosis, vascular hypertrophy, or angioma.

In some embodiments, the method may further include generating arelationship between a condition of the blood vessel and a time phase,based on the condition of the blood vessel of the first vascular modelat the first time phase and the condition of the blood vessel of thesecond vascular model at the second time phase.

In some embodiments, the method may further include determining, basedon the relationship between the condition of the blood vessel and time,a condition of the blood vessel at a third time phase.

In some embodiments, the determining the condition of the blood vesselof the first vascular model at the first time phase, or the determininga condition of the blood vessel of the second vascular model at thesecond time phase, comprises employing a method of computational fluiddynamics (CFD).

In one aspect of the present disclosure, a system including at least oneprocessor and a storage device is provided. The system may include areceiving module. The receiving module may be configured to obtain afirst image at a first time phase and a second image of at a second timephase. The system may further include a multi-time phase featuregeneration module. The multi-time phase feature generation module may beconfigured to: select a first vascular region from the first image,wherein the first vascular region includes a blood vessel; select asecond vascular region from the second image, wherein the secondvascular region includes at least a part of the blood vessel; generate afirst vascular model, wherein the first vascular model corresponds tothe first vascular region; generate a second vascular model, wherein thesecond vascular model correspond to the second vascular region; set aboundary condition of the first vascular model and a boundary conditionof the second vascular model; determine a condition of the blood vesselof the first vascular model at the first time phase, according to theboundary condition of the first vascular model; correlate the firstvascular model and the second vascular model, based on the condition ofthe blood vessel at the first time phase; determine a condition of theblood vessel of the second vascular model at the second time phase,according to the result of correlation and the boundary condition of thesecond vascular model.

In one aspect of the present disclosure, a method implemented on atleast one device including a processor and a storage is provided. Themethod may include: obtaining a 2-dimensional image, wherein the2-dimensional image includes one or more regions of interest; extractinga plurality of boundary points of the one or more regions of interest;determining a first region and a second region, according to theboundary points; generating, based on a first grid division controlcondition, grids of the first region; generating, based on a second griddivision control condition, grids of the second region, wherein thesecond grid division control condition differs from the first griddivision control condition; and analyzing, according to the grids of thefirst region and the grids of the second grid, the one or more regionsof interest.

In some embodiments, the one or more regions of interest may include atleast one of a coronary artery, an abdominal artery, a cerebral artery,or a lower extremity artery.

In some embodiments, the grids of the first region or the grids of thesecond region are generated based on Delaunay triangulation (DT).

In some embodiments, the first grid division control condition mayinclude a first area constraint condition.

In some embodiments, the first area constraint condition may includelimiting the area of all grids to be smaller than or equal to an areaconstraint value.

In some embodiments, the second grid division control condition mayinclude a second area constraint condition that differs from the firstarea constraint condition.

In some embodiments, the method may further include determining,according to the plurality of boundary points, a third region, whereinthe third region is not divided into grids.

In some embodiments, the analyzing the region of interest (ROI)comprises analyzing a dynamic parameter of the region of interest, byemploying a method of computational fluid dynamics (CFD). The dynamicparameter may include blood velocity, blood pressure, wall stress of theblood vessel, wall shear stress (WSS) of the blood vessel, fractionalflow reserve (FFR), or value of coronary flow reserve (CFR).

In some embodiments, the method may further include: obtaining a3-dimensional image, wherein the 3-dimensional image may include the oneor more regions of interest; and generating 3-dimensional gridscorresponding to the 3-dimensional image based on the grids of the firstregion and the grids of the second region.

In another aspect of the present disclosure, a system including at leastone processor and a storage device is provided. The system may include areceiving module. The receiving module may be configured to: obtain a2-dimensional image, wherein the 2-dimensional image may include one ormore regions of interest. The system may further include a multi-timephase feature generation module. The multi-time phase feature generationmodule may be configured to: extract a plurality of boundary points ofthe one or more regions of interest; determine, according to theplurality of boundary points, a first region and a second region;generate, based on a first grid division control condition, grids of thefirst region; generate, based on a second grid division controlcondition, grids of the second region, wherein the second grid divisioncontrol condition differs from the first grid division controlcondition; and analyzing, according to the grids of the first region andthe grids of the second region, the one or more regions of interest.

In another aspect of the present disclosure, a method to be implementedon at least one device including a processor and a storage is provided.The method may include: obtaining vascular images of multiple timephases, including a first vascular image at a first time phase and asecond vascular image of at a second time phase, wherein the vascularimages of multiple time phases correspond to a same blood vessel or apart thereof; generating multiple vascular models, wherein the multiplevascular models correspond to the vascular images of multiple timephases; obtaining multiple conditions, including a first vascularcondition and a second vascular condition, of the blood vessel or thepart thereof according to the multiple vascular models, wherein thefirst vascular condition corresponds to the first vascular image, andthe second vascular condition corresponds to the second vascular image;obtaining a relationship between the condition of the blood vessel orthe part thereof and time, according to the multiple conditions of theblood vessel or the part thereof; and obtaining a third vascularcondition of the blood vessel or the part thereof, according to therelationship. Herein, a “vascular image” corresponding to a “bloodvessel” may refer to that the vascular image including an image of theblood vessel. For example, a blood vessel may include an aortic or apart thereof, a coronary or a part thereof, etc.

In some embodiments, the blood vessel may include at least one of acoronary artery, an abdominal artery, a cerebral artery, or a lowerextremity artery.

In some embodiments, the third vascular condition may be an averagefractional flow reserve (FFR).

In some embodiments, the method may further include: correlating themultiple vascular models; and analyzing, according to the result ofcorrelation, the multiple conditions of blood vessels employing a methodof computational fluid dynamics (CFD).

In some embodiments, the correlating the multiple vascular models mayinclude correlating at least two of the vascular models at entrances,bifurcation segments, stenosis segments, or exits of the blood vessel.

In some embodiments, the method may further include generating grids ofthe multiple vascular models.

In some embodiments, the method may further include matching the gridsof the multiple vascular model.

In another aspect of the present disclosure, a system including at leastone processor and a storage device is provided. The system may include areceiving module. The receiving module may be configured to: obtainvascular images at multiple time phases, including a first vascularimage at a first time phase and a second vascular image at a second timephase, wherein the vascular images at multiple time phases maycorrespond to a same blood vessel or a part thereof respectively. Thesystem may further include a multi-time phase feature generation module.The multi-time phase feature generation module may be configured to:generate multiple vascular models, wherein the multiple vascular modelsmay correspond to the vascular images at multiple time phases; obtainmultiple conditions, including a first vascular condition and a secondvascular condition, of the blood vessel or the part thereof, accordingto the multiple vascular models, wherein the first vascular conditionmay correspond to the first vascular image, and the second vascularcondition may correspond to the second vascular image; obtain arelationship between the condition of the blood vessel or the partthereof and time, according to the multiple conditions of the bloodvessel or the part thereof; and obtain a third vascular condition of theblood vessel or the part thereof, according to the relationship.

In another aspect of the present disclosure, a method implemented on atleast one device including a processor and a storage is provided. Themethod may include: obtain a first vascular model, wherein the firstvascular model may correspond to a blood vessel including a firstregion; obtain one or more parameters of the first vascular model;determine, according to the one or more parameters of the first vascularmodel, a position of the first region of the first vascular model;generate a second vascular model including the blood vessel or a partthereof, wherein the first region of the blood vessel of the secondvascular model may be modified compared to the first region of the bloodvessel of the first vascular model; obtain a boundary condition of thesecond vascular model; determine, according to the boundary condition ofthe second vascular model, a parameter of the second vascular model;determine, according to the parameter of the second vascular model, aboundary conditions of the first vascular model; and obtain, accordingto the boundary condition of the first vascular model, a blood flowcondition of the first vascular model.

In some embodiments, the first region may include a region of vascularstenosis, vascular hypertrophy, or angioma.

In some embodiments, the one or more parameters of the first vascularmodel may include a cross-sectional area of the blood vessel.

In some embodiments, the modifying the first region of the firstvascular model may include dilating or narrowing the blood vessel.

In some embodiments, the boundary condition of the second vascular modelmay include blood pressure, blood velocity, blood viscosity, pressure,or wall stress, of an entrance, an exit, or a side wall of the bloodvessel of the second vascular model.

In some embodiments, the parameter of the second vascular model mayinclude flow resistance, blood velocity, blood pressure, wall stress ofthe blood vessel, wall shear stress (WSS) of the blood vessel, orfractional flow reserve (FFR).

In some embodiments, the determining a parameter of the second vascularmodel may include performing a computational fluid dynamics (CFD)analysis.

In some embodiments, the method may further include determining adynamic parameter of the first vascular model according to the boundarycondition of the first vascular model.

In another aspect of the present disclosure, a system including at leasta processer and a storage device is provided. The system may include areceiving nodule. The receiving module may be configured to obtain afirst vascular model, wherein the first vascular model may include afirst region; obtain one or more parameters of the first vascular model.The method may further include a multi-time phase feature generationmodule. The multi-time phase feature generation module may be configuredto: determine, according to the one or more parameters of the firstvascular model, a position of the first region of the first vascularmodel; generate a second vascular model by modifying the first region ofthe first vascular model; obtain a boundary condition of the secondvascular model; determine, according to the boundary conditions of thesecond vascular model, a parameter of the second vascular model;determine, according to the parameter of the second vascular model, aboundary condition of the first vascular model; and obtain, according tothe boundary condition of the first vascular model, a blood flowcondition of the first vascular model.

Some of appended features of the present disclosure are illustrated inthe following description. The appended features of the presentdisclosure are obvious to those skilled in the art, under the teachingof the description with appended drawings or the productions/operationsof the embodiments. The features of the present disclosure may beimplemented and realized by the practice or use of various methods,means and combinations of various aspects of the embodiments describedbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of schematicembodiments. These schematic embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting schematic embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1A illustrates a schematic diagram of a blood flow conditionanalysis system according to some embodiments of the present disclosure;

FIG. 1B illustrates another schematic diagram of a blood flow conditionanalysis system according to some embodiments of the present disclosure;

FIG. 2 illustrates a structure of a computing device that can implementa specific system according to some embodiments of the presentdisclosure;

FIG. 3 illustrates a schematic diagram of a mobile device that canimplement a specific system according to some embodiments of the presentdisclosure;

FIG. 4A illustrates a schematic diagram of an exemplary processingdevice according to some embodiments of the present disclosure;

FIG. 4B is a flow chart illustrating a process for processing multi-timephase features according to some embodiments of the present disclosure;

FIG. 5 illustrates a schematic diagram of an exemplary multi-time phasefeature generation module according to some embodiments of the presentdisclosure;

FIG. 6 is a flow chart illustrating a process for obtaining multi-timephase features according to some embodiments of the present disclosure;

FIG. 7 is a schematic diagram illustrating a process for obtainingmulti-time phase features according to some embodiments of the presentdisclosure;

FIG. 8 is a flow chart illustrating a process for setting a boundarycondition according to some embodiments of the present disclosure;

FIG. 9 is a schematic diagram of a blood flow module according to someembodiments of the present disclosure;

FIG. 10 is a flow chart illustrating a process for a grid divisionaccording to some embodiments of the present disclosure;

FIG. 11 illustrates a schematic diagram of a process for a grid divisionof a boundary region according to some embodiments of the presentdisclosure;

FIG. 12 illustrates a flow chart of a process for a grid divisionaccording to some embodiments of the present disclosure;

FIG. 13 illustrates a flow chart of a process for obtaining hemodynamicparameters corresponding to a point according to some embodiments of thepresent disclosure; and

FIG. 14 illustrates a schematic diagram of a process for obtaining ahemodynamic parameter corresponding to a point according to someembodiments of the present disclosure.

DETAILED DESCRIPTION

In order to illustrate the technical solutions related to theembodiments of the present disclosure, brief introduction of thedrawings referred to in the description of the embodiments is providedbelow. Obviously, drawings described below are only some examples orembodiments of the present disclosure. Those having ordinary skills inthe art, without further creative efforts, may apply the presentdisclosure to other similar scenarios according to these drawings.Unless stated otherwise or obvious from the context, the same referencenumeral in the drawings refers to the same structure and operation.

As used in the disclosure and the appended claims, the singular forms“a,” “an,” and “the” include plural referents unless the content clearlydictates otherwise. It will be further understood that the terms“comprises,” “comprising,” “includes,” and/or “including” if used in thedisclosure, specify the presence of stated steps and elements, but donot preclude the presence or addition of one or more other steps andelements.

Some modules of the system may be referred to in various ways accordingto some embodiments of the present disclosure, however, any amount ofdifferent modules may be used and operated in a client terminal and/or aserver. These modules are intended to be illustrative, not intended tolimit the scope of the present disclosure. Different modules may be usedin different aspects of the system and method.

According to some embodiments of the present disclosure, flow charts areused to illustrate the operations performed by a data processing system.It is to be expressly understood, the operations above or below may ormay not be implemented in order. Conversely, the operations may beperformed in inverted order, or simultaneously. Besides, one or moreother operations may be added to the flowcharts, or one or moreoperations may be omitted from the flowchart.

In the process of image processing, “image segmentation”, “imageextraction”, and “image classification” may each means selecting animage that satisfies a specific condition from a large region and may beused interchangeably. According to some embodiments of the presentdisclosure, an imaging system may include one or more formats. Theformats may include but are not limited to digital subtractionangiography (DSA), magnetic resonance imaging (MRI), magnetic resonanceangiography (MRA), computed tomography (CT), computed tomographyangiography (CTA), ultrasonic scanning (US), positron emissiontomography (PET), single photon mission computed tomography (SPECT),SPECT-MR, CT-PET, CE-SPECT, DSA-MR, PET-MR, PET-US, SPECT-US, TMS-MR,US-CT, US-MR, X-ray-CT, X-ray-PET, X-ray-US, video-CT, video-US, or thelike, or any combination thereof. In some embodiments, a subject ofimage scanning may include an organ, a body, an object, an injuredsection, a tumor, or the like, or any combination thereof. In someembodiments, a subject of image scanning may include a brain, a thorax,an abdomen, an organ, a bone, a vessel, or the like, or any combinationthereof. In some embodiments, a subject of image scanning may includeblood vessels of one or more tissues. In some embodiments, the image mayinclude a 2-dimensional image and/or a 3-dimensional image. A smallestdivisible element of the 2-dimensional image may be a pixel. A smallestdivisible element of the 3-dimensional image may be a voxel. The3-dimensional image may include a series of 2-dimensional slices and/or2-dimensional layers.

A process of image segmentation may be performed based on featurescorresponding to the pixels (or voxels) of an image. In someembodiments, the features corresponding to the pixels (or voxels) mayinclude texture, grayscale, average grayscale, signal strength, colorsaturation, contrast, brightness, or the like, or any combinationthereof. In some embodiments, a spatial position feature correspondingto the pixels (or voxels) may be used in the process of imagesegmentation.

The present disclosure relates to a method and system for obtainingblood flow conditions. In a process of determining blood flowconditions, images of multiple time phases may be obtained, and multiplevascular models corresponding to multiple time phases may be generated.The multiple vascular models may be correlated to obtain boundaryconditions of the multiple vascular models. According to the boundaryconditions, condition of blood vessel of the multiple vascular modelsmay be determined.

FIG. 1A illustrates a schematic diagram of a blood flow conditionanalysis system 100 according to some embodiments of the presentdisclosure. The blood flow condition analysis system 100 may include adata collection device 110, a processing device 120, a storage device130, and a communication device 140. The data collection device 110, theprocessing device 120, the storage device 130, and the communicationdevice 140 may communicate with each other via a network 180.

The data collecting device 110 may be configured to collect data. Thedata may include image data, object's features, etc. In someembodiments, the data collecting device 110 may include an imagingdevice. The imaging device may collect the image data. The imagingdevice may be a magnetic resonance imaging (MRI) device, a computedtomography (CT) device, a positron emission computed tomography (PET)device, a b-scan ultrasonography device, an ultrasonic diagnosticdevice, a thermal texture mapping (TTM) device, a medical electronicendoscope (MEE) device, or the like, or any combination thereof. Theimage data may include images or data of a blood vessel, a tissue, or anorgan of an object. In some embodiments, the data collection device mayinclude an object feature collection device. The object featurecollection device may collect object features such as heart rate, heartrhythm, blood pressure, blood velocity, blood viscosity, cardiac output,myocardial mass, vascular flow resistance, and/or other object featuresassociated with blood vessels, tissues or organs. In some embodiments,the object feature collection device may obtain age, height, weight,gender, or other features of the object. In some embodiments, the imagedata and the object features may be multi-time phase data. For example,the multi-time phase data may include data obtained from a same orsimilar position of an object at different time points or time phases.In some embodiments, the object feature collection device may beintegrated in the imaging device so that the image data and the object'sfeatures may be collected simultaneously. In some embodiments, the datacollection device 110 may send the collected data to the processingdevice 120, the storage device 130, and/or the communication device 140via the network 180.

The processing device 120 may process data. The data may be collected bythe data collection device 110. The data may also be obtained from thestorage device 130, the communication device 140 (e.g., input data of auser), or from a cloud or an external device via the network 180. Insome embodiments, the data may include image data, object's featuresdata, user input, etc. The processing of the data may include selectinga region of interest from the image data. The region of interest may beselected solely by the processing device 120, or selected based on userinput. In some embodiments, the region of interest may include a bloodvessel, a tissue, an organ, etc. For example, the region of interest maybe an artery, such as a coronary artery, an abdominal artery, a brainartery, a lower extremity artery, etc. The processing device 120 mayfurther segment the region of interest. The technique of imagesegmentation may include a technique based on edges (e.g., a Perwittoperator, a Sobel operator, a gradient operator, a Kirch operator,etc.), a technique based on regions (e.g., a region growing technique, athreshold technique, a clustering technique, etc.), or other techniquesbased on fuzzy sets, a neural network, etc.

The processing device 120 may reconstruct a model that corresponds tothe region of interest. The model may be selected based on the object'sfeatures, features of the region of interest, etc. For example, ifselecting the coronary artery as the region of interest, the processingdevice 120 may segment an image that includes a coronary artery toextract an image of the coronary artery. The processing 120 mayreconstruct the model according to the object features, general featuresof the coronary artery, image features of the coronary artery, etc. Thereconstructed model may correspond to a vascular shape or a blood flowshape of the coronary artery. After reconstructing the model of theregion of interest, the processing device 120 may preform analysis andcomputation based on the model. Techniques of analysis and computationmay include computed fluid dynamics, etc.

In some embodiments, the processing device 120 may obtain data atmultiple time phases. For example, the processing device 120 may obtainimages of the coronary artery of an object at five different timephases. In such situation, the processing device 120 may reconstructmodels corresponding to regions of interest (e.g., an entire coronaryartery, a branch of the coronary artery, a cross section of a bloodentrance of the coronary artery, etc.) at different time phasesrespectively. The processing device 120 may then analyze and compute themodels in sequence. In some embodiments, the processing device 120 maygenerate grids or meshes (also referred to as grid process or griddivision) on the models at different time phases. The processing device120 may correlate the grid processed models with each other to reducecomputation load and improve computational accuracy. Techniques ofcorrelating and grid processing may be found elsewhere in the presentdisclosure, for example, in FIG. 6, FIG. 11 and their correspondingdescriptions. In some embodiments, the analysis and computation resultmay include a physical state and a coefficient/parameter of a bloodvessel, a tissue, or an organ. For example, a result of analysis andcomputation of the model of coronary artery may include a hemodynamicparameter such as blood velocity, blood pressure, wall stress of theblood vessel, wall shear stress (WSS) of the blood vessel, fractionalflow reserve (FFR), coronary flow reserve (CFR), or the like, or anycombination thereof. In some embodiments, the processing device 120 maygenerate a relationship between the physical state and/or thecoefficient/parameter and time phase (e.g., changes of hemodynamicparameter with time). In some embodiments, the relationship may begenerated based on the results of analysis and computation at differenttime phases. The relationship may be represented by a curve or a table.The processing device 120 may obtain physical states and/orcoefficients/parameters of the regions of interest at any time phasebased on the curve or the table.

In some embodiments, the processing device 120 may denoise or smoothobtained data or a processing result. In some embodiments, theprocessing device 120 may send the obtained data or the processingresult to the storage device 130 for storing, or the communicationdevice 140 for displaying. The processing result may be an intermediateresult generated in the process (e.g., a model of a region of interest),or a final result of the process (e.g., an analyzed and computedhemodynamic parameter, etc.). In some embodiments, the processing device120 may be one or more processing units or devices, such as centralprocessing units (CPUs), graphics processing units (GPUs), digitalsignal processors (DSPs), systems on a chip (SoC), microcontroller units(MCUs), etc. In some embodiments, the processing device 120 may be aspecially designed processing unit or device with specific functions.The processing device 120 may be local, or remote with respect to thedata collection device 110.

The storage device 130 may store data or information. The data orinformation may include data obtained by the data collection device 110,processing results or control instructions generated by the processingdevice 120, user input received by the communication device 140, etc.The storage device 130 may be one or more storage mediums withread/write functions. The storage device 130 may include but not limitedto a static random access memory (SRAM, a random-access memory (RAM), aread-only memory (ROM), a hard disk, a flash memory, etc. In someembodiments, the storage device 130 may be a remote storage device, suchas a cloud disk, etc.

The interactive 140 may be configured to receive, send, and/or displaydata or information. The received data or information may include thedata obtained by the data collection device 110, the processing resultsgenerated by the processing device 120, the data stored by the storagedevice 130, etc. For example, the data or information displayed by thecommunication device 140 may include an actual image 150 of acardiovascular obtained by the data collection device 110, acardiovascular model 160 reconstructed by the processing device 120based on the actual image 150, a coronary artery model extracted fromthe cardiovascular model 160 by the processing device 120, etc. Theformats of display may include but is not limited to a 2-dimensional or3-dimensional medical image, a geometric model and its grid processedresult, a vector diagram (e.g., a velocity vector), a contour map, afilled contour map (cloud chart), an XY scatter plot, a particletrajectory map, a simulated flow effect, or the like, or any combinationthereof. As another example, the data or information sent by thecommunication device 140 may include input information of a user. Thecommunication device 140 may receive one or more operating parameters ofthe processing device 120 input by the user, and send the operatingparameters to the processing device 120.

In some embodiments, the communication device 140 may include a userinterface. The user may provide a user input to the communication device140 by specific interactive apparatuses such as a mouse, a keyboard, atouchpad, a microphone, etc. For example, the user may click on themodel displayed by the communication device 140 and select a region ofinterest of the model. As another example, the user may select anyposition of the vascular model displayed by the communication device140. The communication device 140 may then obtain a blood velocity, ablood pressure, a blood flow, etc. of that position from the processingdevice 120 and display them.

In some embodiments, the communication device 140 may be a device withdisplaying function, such as a screen. In some embodiments, thecommunication device 140 may have some or all functions of theprocessing device 120. For example, the communication device 140 mayimplement operations (e.g., smoothing, denoising, changing colors, etc.)to the results generated by the processing device 120. Merely by way ofexample, the operation of changing colors may include transferring agrayscale image to a color image, or transferring a color image to agrayscale image. In some embodiments, the communication device 140 andthe processing device 120 may be an integrated device. The integrateddevice may implement functions of both the processing device 120 and thecommunication device 140. In some embodiments, the communication device140 may include a desktop computer, a server, a mobile device, etc. Themobile device may include a laptop computer, a tablet computer, an iPad,a built-in device of a vehicle (e.g., a motor vehicle, a ship, anairplane), a wearable device, etc. In some embodiments, thecommunication device 140 may include or is connected to a displayapparatus, a printer, a fax machine, etc.

The network 180 may be used for internal communication of thein bloodflow condition analysis system 100. The network 180 may also beconfigured to receive information from or send information to theexternal devices outside the system 100. In some embodiments, the datacollection device 110, the processing device 120, and the communicationdevice 140 may be connected to the network 180 via a wired connection, awireless connection, or a combination thereof. The network 180 may be asingle network or a combination of networks. In some embodiments, thenetwork 180 may include but is not limited to a local area network(LAN), a wide area network (WAN), a public network, a proprietarynetwork, a wireless local area network (WLAN), a virtual network, anurban metropolitan area network, a public switched telephone network(PSTN), or the like, or any combination thereof. In some embodiments,the network 180 may include multiple network access points, such as awired or wireless access point, a base station or network switchedpoint, etc. Through these access points, any data source may beconnected to the network 180 and transmit information via the network180.

FIG. 1B illustrates another schematic diagram of a network environmentincluding a blood flow condition analysis system 100 according to someembodiments of the present disclosure. FIG. 1B is similar to FIG. 1A. InFIG. 1B, the processing device 120 may be directly connected to the dataconnection device 110. The data connection device 110 may not directlyconnect to the network 180.

The above description of the present disclosure is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,modules may be combined in various ways, or connected with other modulesas sub-systems. Various variations and modifications may be conductedunder the teaching of the present disclosure. However, those variationsand modifications may not depart the spirit and scope of thisdisclosure. For example, the data connection device 110, the processingdevice 120, and the communication device 140 may directly exchangeinformation with each other without the network 180. As another example,the devices may exchange information by a removable storage device oranother intermediate medium.

FIG. 2 illustrates a structure of a computing device 200 that canimplement a specific system according to some embodiments of the presentdisclosure. The computing device 200 may implement a specific system ofthe present disclosure. The specific system of the present disclosuremay use a functional diagram to describe a hardware platform including auser interface. The computing device 200 may configured to implement oneor more components, modules, units, sub-units (e.g., the processingdevice, the interactive device, etc.) of the blood flow conditionanalysis system 100. The one or more components, modules, units,sub-units (e.g., the processing device, the interactive device, etc.) ofthe blood flow condition analysis system 100 may be implemented by thecomputing device 200 by a hardware device, a software program, afirmware, or any combination thereof of the computing device 200. Thecomputing device 200 may be a general purpose computing device, or aspecific purpose computing device. The computing devices may beconfigured to implement the specific system of the present disclosure.For brevity, the FIG. 2 illustrates only one computing device. Accordingto some embodiments, functions of processing and pushing information maybe processing loads of a decentralized system implemented on a set ofsimilar platforms in a distributed manner.

As showed in FIG. 2, the computing device 200 may include an internalcommunication bus 210, a processor 220, a read-only memory (ROM) 240, arandom-access memory (RAM) 240, a communication port 250, aninput/output component 260, a hard disk 270, a user interface 280, etc.The internal communication bus 210 may be configured to implement datacommunications between components of the computing device 200. Theprocessor 220 may implement program instructions to complete one or morefunctions, components, modules, units, sub-units of the blood flowcondition analysis system 100 disclosure in the present disclosure. Theprocessor 220 may include one or more processors. The commination port250 may be configured to implement data communications (e.g., via thenetwork 180) between the computing device 200 and other parts (e.g., thedata connection device 110) of the blood flow condition analysis system100. The computing device 200 may include different forms of programstorage unit and data storage unit, such as a hard disk 270, a read-onlymemory (ROM) 230, a random access memory (RAM) 240, various data filesused by a computing device for processing or communication, a possibleprogram instruction implemented by the processor 220. The input/outputcomponent 260 may support inputting/outputting data stream between thecomputing device 200 and other components (e.g., the user interface280), and/or other components of the blood flow condition analysissystem 100. The computing device 200 may send and receive informationand data by the communication port 250 via the network 180.

FIG. 3 illustrates a schematic diagram of a mobile device that canimplement a specific system according to some embodiments of the presentdisclosure. In some embodiments, a user device that is configured todisplay information related to an interactive position may be a mobiledevice 300. The mobile device 300 may include a smart phone, a tabletcomputer, a music player, a portable game console, a GPS receiver, awearable calculating device (e.g. glasses, watches, etc.), etc. Themobile device 300 may include one or more central processing units(CPUs) 340, one or more graphical processing units (GPUs) 330, a display320, a memory 360, an antenna 310 (e.g. a wireless communication unit),a storage unit 390, and one or more input/output (I/O) devices 350.Moreover, the mobile device 300 may also include any other suitablecomponent that includes but is not limited to a system bus or acontroller (not shown in FIG. 3). As shown in FIG. 3, a mobile operatingsystem 370 (e.g. iOS, Android, Windows Phone, etc.) and one or moreapplications 380 may be loaded from the storage unit 390 to the memory360 and implemented by the CPUs 340. The application 380 may include abrowser or other mobile applications configured to receive and processinformation related to the images or blood flow condition analyses inthe mobile device 300. The communication information related to theimages or blood flow condition analyses between the user and the one ormore components of the system 100 may be obtained through the I/O device350, and provide the information to the processing device 120 and/orother modules or units of the system 100, e.g. the network 180.

FIG. 4A illustrates a schematic diagram of an exemplary processingdevice according to some embodiments of the present disclosure. Theprocessing device 120 may include a receiving module 410, a controllingmodule 420, a multi-time phase feature processing module 440, and anoutput module 450.

The receiving module 410 may obtain image data, object's features, etc.from the data collection device 110 and/or the storage device 130. Theimage data may include an image or data of a blood vessel, a tissue, oran organ of an object. The object's features may include heart rate,heart rhythm, blood pressure, blood velocity, blood viscosity, cardiacoutput, myocardial mass, vascular flow resistance, and other object'sfeatures related to the blood vessel, the issue or the organ. Theobject's features may also include age, height, weight, gender, or otherfeatures of the object. In some embodiments, the image data and theobject's features may be multi-time phase data. For example, themulti-time phase data may include data obtained from a same or similarposition of the object at different time points or time phases.

The controlling module 420 may generate a control instruction. Thecontrol instruction may instruct another module to implement anoperation such as inputting, outputting, storing, processing, etc. Forexample, the control instruction may instruct the receiving module 410to receive needed data. As another example, the instruction may instructthe multi-time phase feature generation module 430 to generate amulti-time phase feature.

The multi-time phase feature generation module 430 may be configured togenerate a multi-time phase feature. The multi-time phase feature mayinclude a multi-time phase model, a multi-time phase parameter, amulti-time phase boundary condition, a multi-time phase analysis result,etc. More particularly, for example, the multi-time phase generationmodule 430 may select regions of interest from the multi-time phaseimage data. The region of interest may be selected solely by multi-timephase feature generation module 430, or selected based on user input. Insome embodiments, the region of interest may be a blood vessel, atissue, an organ, etc. For example, the region of interest may includean artery(s), such as a coronary artery, an abdominal artery, a brainartery, a lower extremity artery, etc. The regions of interest selectedfrom the multi-time phase image may correspond to the region ofinterest. For example, the region of interest may include at least partsof a same blood vessel, a tissue, an organ, etc., as the region ofinterest. The multi-time phase generation module 430 may further segmentthe region of interest. The technique of image segmentation may includea technique based on edges (e.g., a Perwitt operator, a Sobel operator,a gradient operator, a Kirch operator, etc.), a technique based onregions (e.g., a region growing technique, a threshold technique,clustering technique, etc.), or other techniques based on fuzzy sets, aneural network, etc. In some embodiments, the multi-time phasegeneration module 430 may segment the regions of interest of themulti-time phase image simultaneously. In some embodiments, themulti-time phase generation module 430 may segment the regions ofinterest of the multi-time phase image in sequence.

The multi-time phase generation module 430 may reconstruct a model ofthe region of interest to generate a multi-time phase model. The modelmay be selected based on the object's features, features of the regionof interest, etc. For example, if coronary artery is selected as theregion of interest, the multi-time phase generation module 430 maysegment an image that includes a coronary artery to extract an image ofthe coronary artery. Then the multi-time phase generation module 430 mayreconstruct the model according to the object features, general featuresof the coronary artery, image features of the coronary artery, etc. Thereconstructed model may correspond to a vascular shape or a blood flowshape of the coronary artery. After reconstructing the model of theregion of interest, the multi-time phase generation module 430 may setparameters and boundary conditions, and may implement analysis andcomputation based on the model. The technique of setting the parametersand the boundary conditions may be found elsewhere in the presentdisclosure.

The multi-time phase processing module 440 may process a generatedmulti-time phase computing result (also referred to as post-processing).The processing may include generate a curve or table of a relationshipbetween the computation result of the model and time phase usingcurve-fitting, interpolation, etc. According to the curve or table ofthe relationship, the multi-time phase processing module 440 may furthergenerate an estimated value of the analysis result at any time phase.The steps and results of the post-processing may be found in FIG. 13 andits corresponding descriptions. In some embodiments, the multi-timephase processing module 440 may compare the generated multi-time phasecomputation result (e.g., a vascular condition) and a reference resultto generate a comparison result. The reference result may be stored inthe storage device 130 or the network 180, or input by a user. In someembodiments, the reference result and the related comparison result maybe stored in a table. For example, if the computation result is theblood velocity, the reference result may be a relationship between arange of the blood velocities and their corresponding degree of risk.The degree of risk may be divided into normal, warning, dangerous,extremely dangerous, etc. In some embodiments, the user may input therelationship manually based on clinical experiences. In someembodiments, the comparison may be a comparison of computation resultsof a same object at different time periods.

The output module 450 may output the generated multi-time phasecomputation result or data. For example, the output module 450 may sendthe multi-time phase result or features to the storage device 130 forstoring, or to the communication device 140 for displaying. In someembodiments, the multi-time phase feature processing module 440 or theoutput module 450 may denoise or smooth the multi-time phase feature orthe computation result before outputting. The multi-time phasecomputation result may be a generated intermediate result (e.g., a modelof a region of interest), or a generated final result (e.g., an analyzedand computed hemodynamic parameter, a curve or table of a relationshipbetween the computation result and time phase, etc.).

FIG. 4B is a flow chart illustrating a process for processing multi-timephase features according to some embodiments of the present disclosure.In some embodiments, the process 400 may be implemented by theprocessing module 120.

In 462, one or more control instructions may be generated. In someembodiments, 462 may be implemented by the controlling module 420. Thecontrol instructions may instruct implementation of other steps in theprocess 400.

In 464, multi-time phase data may be received. In some embodiments, 464may be implemented by the receiving module 410. The multi-time phasedata may include multi-time phase image data and multi-time phaseobject's features. In some embodiments, the multi-time phase object'sfeatures may be continuous object's features or features curves on time.

In 466, multi-time phase feature may be generated. In some embodiments,466 may be implemented by the multi-time phase feature generation module430. The multi-time phase feature may include a multi-time phase model,a multi-time phase parameter, a multi-time phase boundary condition, amulti-time phase analysis result, etc.

In 468, the generated multi-time phase feature may be processed. In someembodiments, 468 may be implemented by the multi-time phase featureprocessing module 440. The processing may include generating a curve ortable of a relationship between the multi-time phase feature and timephase by employing a technique such as fitting, interpolation, etc.

In 470, a multi-time phase feature or a processing result may be output.In some embodiments, 470 may be implemented by the output module 450. Insome embodiments, 468 may be omitted, and the generated multi-time phasefeature may be directly outputted.

FIG. 5 illustrates a schematic diagram of an exemplary multi-time phasefeature generation module according to some embodiments of the presentdisclosure. The multi-time phase feature generation module 430 mayinclude a data acquisition unit 510, a parameter setting unit 520, acomputing unit 530, a grid generation unit 540, a matching unit 550, aregion selection unit 560, an output unit 570, and a determination unit580.

The data acquisition unit 510 may obtain data from other units of themulti-time phase feature generation module 430, other devices/modules ofthe blood flow condition analysis system 100, or externaldevices/modules. The data may include image data, object's features,user input, etc. The image data may include images or data of a bloodvessel, a tissue, an organ of an object. The object's features mayinclude heart rate, heart rhythm, blood pressure, blood velocity, bloodviscosity, cardiac output, myocardial mass, vascular flow resistance,and other data related to the blood vessel, the tissue or the organ. Insome embodiments, the image data and the object's features may bemulti-time phase data. In some embodiments, the data acquisition unit510 may obtain processed data (e.g., a reconstructed vascular model,etc.) from the storage device 130. In some embodiments, the dataacquisition unit 510 may preprocess the obtained image data. Thepreprocessing may include image enhancement, image denoising, imagesmoothing, etc.

The parameter setting unit 520 may select a model. The parameter settingunit 520 may also set a parameter and a boundary condition of the model.The model selection may include selecting a suitable blood viscositymodel and a velocity boundary model based on a lesion region that needsto be analyzed (e.g. a region of interest) and the object's features(e.g., blood viscosity, etc.). The blood viscosity model may include aNewtonian fluid model, a non-Newtonian fluid model, and otheruser-defined fluid model. The Newtonian fluid model may be used tosimulate a region of an object with a constant blood viscosity. Thenon-Newtonian fluid model may be used to simulate a region of an objectwith a changing blood viscosity. The velocity boundary model may includea parabolic model, a hyperbolic model, an elliptical model, an averageflow model, a Womersley distribution model, a Reynolds model, a mixturemodel, etc. In some embodiments, the parameter setting may includesetting a parameter of a selected model, such as a blood viscositycoefficient of the Newtonian model, a blood density of the Newtonianmodel, time steps of a simulated computation, a time step length of thesimulated computation, etc.

The setting of boundary conditions may include setting an initialcondition and a limit condition of a boundary region. The boundaryregion may refer to an edge region of a region of interest. For example,if a selected region of interest is a blood flow region corresponding toa vascular region or a blood vessel, the boundary region may be an exit,an entrance, a vascular wall, etc. The set boundary condition mayinclude blood pressure, blood velocity, flow resistance, pressureintensity, stress, etc. In some embodiments, an internal or externalstorage device of the storage device 130 or the blood flow conditionanalysis system 100 may include a database of boundary conditions. Theuser or the parameter setting unit 520 may set a boundary condition orselect a boundary condition from the database of boundary conditionsbased on the object's features. In some embodiments, the user or theparameter setting unit 520 may select a low order coupling model as theboundary condition based on the region of interest. The low ordercoupling model may choose an empirical model of a region or a tissue asthe boundary condition, wherein the region or the tissue may be coupledwith the region of interest. The low order coupling model may be asecond order model, a first order model, a zero order model (i.e., acentralized parameter model), or a combination thereof.

The computing unit 530 may be configured to compute data or informationgenerated by other units of the multi-time phase feature generationmodule 430. In some embodiments, the computing unit may generate acorresponding model based on the image data. The model may be generatedbased on a model type and a parameter selected by the parameter settingunit 520. In some embodiments, the computing unit 530 may analyze andcompute a model after reconstructing the model of a region of interest.The techniques used in the analysis and computation may include computedfluid dynamics, etc. In some embodiments, results of analysis andcomputation may include a physical state and a coefficient/parameter ofa blood vessel, a tissue, or an organ. For example, a result ofanalyzing and computing the coronary artery model may include ahemodynamic parameter such as blood velocity, blood pressure, wallstress of the blood vessel, wall shear stress (WSS) of the blood vessel,fractional flow reserve (FFR), coronary flow reserve (CFR), or the like,or any combination thereof of the coronary artery.

In some embodiments, the information and data obtained by the computingunit 530 may be a multi-time phase information and data. The computingunit 530 may analyze and compute the multi-time phase information anddata respectively. In some embodiments, the computing unit 530 maygenerate a relationship between the physical state and/or relevantcoefficient/parameter and the time phase, according to the result ofanalysis and computation at different time phases. In some embodiments,the relationship may be represented by a curve or a table. Based on thecurve or the table, the physical state and/or the coefficient/parameterof the region of interest at any time phase may be obtained. In someembodiments, the curve, the table, or the physical state and thecoefficient/parameter of the region of interest at any time phase may besent to an internal or external module/unit of the blood flow conditionanalysis system 100.

The grid generation unit 540 may generate grids of a model. In someembodiments, the grid generation unit 540 may generate a 2-dimensionalor 3-dimensional grid of the model. For example, the grid generationunit 540 may generate a 2-dimensional grid in a boundary region (e.g.,an entrance, an exit, etc.) of the model, and generate 3-dimensionalgrids in other regions of the model. The 3-dimensional grid may bereconstructed based on the 2-dimensional grid. The technique and processrelated to the grid generation may be found in FIG. 10, FIG. 12 andtheir corresponding descriptions.

The matching unit 550 may match multi-time phase data. In someembodiments, the matching unit 550 may correlate models at differenttime phases. The models at different time phases may be grid processedmodels. In some embodiments, the correlation of the models at differenttime phases may include: identifying characteristic regions of themodels at different time phases; correlating the characteristic regionscorresponding to different time phases. For example, if the models atdifferent time phases are blood flow models (e.g., models of blood flowin a blood vessel of interest), the characteristic region may be anentrance region, a bifurcation region, an exit region, a stenosisregion, etc. of the blood flow. Then the matching unit 550 may correlatethe characteristic regions with each other. In some embodiments, thecorrelation of the models corresponding to different time phases mayinclude registering the characteristic regions. In some embodiments, acharacteristic region at different time phases may correspond todifferent numbers of grids. In some embodiments, the grids of thecharacteristic region at different time phases may be correlated by aspecific algorithm or technique. For example, if multiple grids at thefirst time phase correspond to a single grid or fewer grids at thesecond time phase, the matching unit 550 may average the values of themultiple grids at the first time phase, and then correlate the averagedvalues with the value(s) of the grid(s) at the second time phase. Insome embodiments, an initial value (e.g., an initial press intensity, aninitial velocity, etc.) of internal grids (i.e., grids that is notinclude a boundary region of a grid model) may be set to be zero in acalculation at an initial time phase. In subsequent computations, acomputation result of an internal grid at a previous time phase may bemapped or matched to a grid corresponding to the internal grid atcurrent time phase. Then the computation result may be designated as aninitial value at current time phase. In some embodiments, after amatching is completed, the matching unit 550 may prompt a user todetermine whether the matching is accurate. In response to thedetermination that the matching is accurate, a subsequent process may beperformed. In response to the determination that the matching is notaccurate, the user may correct or adjust the matching result. The usermay also select to re-match the grids.

The region selecting unit 560 may select a region of interest in imagedata. The region of interest may be selected solely by the regionselecting unit 560 or selected based on user input. In some embodiments,the selected region of interest may be a blood vessel, a tissue, anorgan, etc. The region selecting unit 560 may further segment the regionof interest of the image. A technique of image segmentation may includea technique based on edges (e.g., a Perwitt operator, a Sobel operator,a gradient operator, a Kirch operator, etc.), a technique based onregions (e.g., a region growing technique, a threshold technique, aclustering technique, etc.), or other techniques based on fuzzy sets, aneural network, etc. The region selecting unit 560 may implement anautomatic or semi-automatic segmentation. For example, if the selectedregion of interest is a coronary artery, an abdominal artery, a brainartery, a lower extremity artery, etc., the region selecting unit 560may segment automatically. If the selected region is a blood vessel orother section that is difficult to be accurately segmented by machines,the region selecting unit 560 may segment semi-automatically with theuser correcting in the segmentation process. In some embodiments, theregion selecting unit 560 may perform the region selection andsegmentation to a 3-dimensional model that is reconstructed based onimage data.

The output unit 570 may send information, data, or a processing resultgenerated by one or more units of the multi-time phase featuregeneration module 430 to other modules or units of the blood flowcondition analysis system 100. For example, the output unit 570 may senda model generated by the computing unit 530 to the communication device140 for displaying. As another example, the output unit 570 may send amodel that is grid processed by the grid generation unit 540 to thestorage device 130 for storing.

The determination unit 580 may implement a logic determination. Forexample, other modules or unit of the blood flow condition analysissystem 100 may send a determination request to the determination unit580. The determination unit 580 may determine a corresponding contentbased on the determination request. If a specific condition is met or adetermination result is generated, the determination unit 580 may sendthe determination result or a corresponding operation instruction to acorresponding module or unit (e.g., a module or unit from which thedetermination request is obtained). For example, the determination unit580 may determine whether a blood vessel to be analyzed by the regionselecting unit 560 is abnormal (e.g., vascular stenosis, aneurysm, etc.)In response to the determination that the blood vessel is abnormal, thedetermination unit 580 may highlight (e.g., with a different color,etc.) the abnormal blood vessel and prompt a user to determine whetherthe abnormal blood vessel satisfies a need of the user. In response tothe determination that the abnormal blood vessel satisfies the need ofthe user, a subsequent operation may be performed. In response to thedetermination that the abnormal blood vessel dose not satisfy the needof the user, the user may manually select the abnormal blood vessel andthe subsequent operation may be performed. For example, the regionselecting unit 560 may send the region of interest selected by the userand a region generated by the region selecting unit 560 to thedetermination unit 580. The determination unit 580 may determine whetherthe region of interest and the region generated by the region selectingunit 560 is the same. In response to the determination that the regionof interest and the region generated by the region selecting unit 560 isthe same, an instruction may be sent to the region selecting unit 560for further segmentation processing. In response to the determinationthat the region of interest and the region generated by the regionselecting unit 560 is different, the user may select and determine againby the communication device 140.

The above description of the present disclosure is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,modules may be combined in various ways, or connected with other modulesas sub-systems. Various variations and modifications may be conductedunder the teaching of the present disclosure. However, those variationsand modifications may not depart the spirit and scope of thisdisclosure. For example, the above unit is described by taking a singlephase as an example, but it should be noted that the data received,processed, or output by the unit may be multi-time phase data. For themulti-time phase data, the above unit may perform similar operations todata at different time phases to generate a multi-time phase feature.For example, the grid generation unit 530 may perform a correspondinggrid processing to a multi-time phase model for generating a multi-timephase grid processed model. As another example, the parameter settingunit 520 may set a corresponding parameter or boundary condition of themulti-time phase model or data.

FIG. 6 is a flow chart illustrating a process for obtaining multi-timephase features according to some embodiments of the present disclosure.In some embodiments, the process 600 may be implemented by themulti-time phase generation module 430.

In 602, multi-time phase data may be obtained. The multi-time phase datamay include multi-time phase image data, multi-time phase object'sfeatures, etc. The multi-time phase image data may include images ordata at multiple different time points of a blood vessel, a tissue, oran organ of an object. The multi-time phase object's features mayinclude heart rate, heart rhythm, blood pressure, blood velocity, bloodviscosity, cardiac output, myocardial mass, vascular flow resistance,and/or other data associated with the blood vessel, the tissues or theorgan of the object. As showed in FIG. 7, 702 may include heart imagesat three time phases. The 704 may be a blood pressure curve of an objectin a cardiac cycle. In some embodiments, the multi-time phase image mayinclude at least part of the same blood vessel, issue, or organ. In someembodiments, the obtained multi-time phase image may be preprocessed.The preprocessing may include image enhancement, image denoising, imagesmoothing, etc.

In 604, a vascular region of interest may be selected within themulti-time phase image. The region of interest may be selected solely bythe region selecting unit 560, or selected based on user input. Theselected regions of interest of images at different time phases may bethe same. In some embodiments, the selected region of interest may befurther segmented. The technique of image segmentation may include atechnique based on edges (e.g., a Perwitt operator, a Sobel operator, agradient operator, a Kirch operator, etc.), a technique based on regions(e.g., a region growing technique, a threshold technique, a clusteringtechnique, etc.), or other techniques based on fuzzy sets, a neuralnetwork, etc. The segmentation may be automatic or semi-automatic. Forexample, if the selected region of interest is a coronary artery, anabdominal artery, a brain artery, a lower extremity artery, etc., theautomatic segmentation may be performed. If the selected region is ablood vessel or other section that is difficult to be accuratelysegmented by machines, the semi-automatic segmentation may be performedwith the user correcting in the segmentation process. In someembodiments, the images at different time phases may be segmented insequence or simultaneously.

In 606, a multi-time phase model of a vascular region may bereconstructed. The multi-time phase model may be a vascular model, or ablood flow model. The vascular region may be a region of a coronaryartery blood vessel, an abdominal artery blood vessel, a brain arteryblood vessel, a lower extremity artery blood vessel, etc. In someembodiments, the vascular region may be part or all of the blood vessel.For example, the vascular region may be an entire coronary artery model,a left coronary artery model, a right coronary artery model, or acoronary branch model (e.g., a left anterior descending (LAD), a leftcircumflex (LCX), a diagonal branch, etc.). As shown in FIG. 7, 708 fromleft to right is an image, a model, and a segmented model, respectively.In some embodiments, a suitable blood viscosity model and a velocityboundary model may be selected based on a lesion region that needs to beanalyzed (e.g., a region of interest) and an object's features (e.g.,blood viscosity, etc.). The blood viscosity model may include aNewtonian fluid model, a non-Newtonian fluid model, and otheruser-defined fluid model. The velocity boundary model may include aparabolic model, a hyperbolic model, an elliptical model, an averageflow model, a Womersley distribution model, a Reynolds model, a mixturemodel, etc. In some embodiments, models corresponding the images atdifferent time phases may be constructed respectively.

In 608, a grid processing may be implemented to the reconstructedmulti-time phase model. In some embodiments, 2-dimensional grids may begenerated at a boundary region (e.g., an entrance, an exit, etc.) of themodel, while 3-dimensional grids may be generated at other regions ofthe model. The 3-dimensional grids may be reconstructed based on the2-dimensional grids. As showed in FIG. 7, 710 may be obtained by gridprocessing the model 708. The technique and process related to the gridgeneration may be found in FIG. 10, FIG. 12 and their correspondingdescriptions.

In 610, a multi-time phase parameter and a boundary condition may beset. In some embodiments, the setting of the parameter may includesetting a parameter of a selected model, such as velocity u, density ρ,blood pressure P, cross-sectional area S, etc. The setting of theboundary condition may include setting an initial condition and a limitcondition of a boundary region. The boundary region may refer to an edgeregion of a region of interest. For example, the boundary region may bean exit, an entrance, a vascular wall, or the like of a blood vessel.The set boundary condition may include blood pressure, blood velocity,flow resistance, pressure intensity, stress, etc., of the boundaryregion. In some embodiments, a low order coupling model may be selectedas the boundary condition based on the region of interest. The low ordercoupling model may choose an empirical model of a region or a tissue asthe boundary condition, wherein the region or the tissue may be coupledwith the region of interest. The low order coupling model may be asecond order model, a first order model, a zero order model (i.e., acentralized parameter model), or a combination thereof of the low ordermodels. As shown in FIG. 7, 712 may be an embodiment of selecting amodel and setting a parameter. The 714 may be an embodiment of setting aboundary condition.

In 612, a current time phase (also referred as an initial time phase iffirst selected) may be selected. In some embodiments, the initial timephase may be selected based on some specific rules. For example, a timephase that a model changes slowly or comparably slowly (e.g., a timephase that is closest to a beginning of heart contraction or an end ofheart diastolic) may be selected as the initial time phase for acoronary artery. The initial time phase may be selected by a machine(e.g., the multi-time phase feature generation module 430) or a user. Ifthe machine and the user do not or cannot select an initial time phase,an arbitrarily selected time phase or a first time phase received by themulti-time phase feature generation module 430 may be designated as theinitial time phase.

In 614, a current time phase (also referred as an initial time phase iffirst selected) may be analyzed. For example, the initial time phase maybe implemented by a computational fluid dynamics (CFD) analysis.According to a predetermined model, boundary condition and parameter,hemodynamic parameters of a 3-dimensional vascular model may beobtained. A control equation based on Euler equations, Navier-Stokesequations, or a Lattice Boltzmann method may be used in obtaining theparameters. A discretization technique such as a finite differencetechnique, a finite volume technique, a finite element technique, aboundary element technique, a spectral technique, a Lattice Boltzmanntechnique, a meshless technique, or the like, or any combination thereofmay be used in obtaining the parameters. A fluid of the flow fieldcomputation that used in obtaining the parameters may be viscous ornon-viscous. The fluid may be compressible or incompressible. The fluidmay be a laminar flow or a turbulent flow. The fluid may be a steadyflow or an unsteady flow. A corresponding control equation or simulationmethod may be selected based on physical features of the simulatedfluid. For example, the Euler equations or the Lattice Boltzmann methodmay be selected for the flow field computation of the non-viscous fluid,while the Navier-Stokes equations or the Lattice Boltzmann method may beselected for the flow field computation of the viscous fluid. Forexample, a computation of the computational fluid dynamics (CFD) for thecoronary artery may use the Navier-Stokes equations:

$\begin{matrix}{{{\frac{\partial\rho}{\partial t} + {\nabla{\cdot \left( {\rho\; u} \right)}}} = 0},{and}} & (1) \\{{\frac{{\partial\rho}\; u}{\partial t} + {\nabla{\cdot \left( {\rho\;{uu}} \right)}}} = {\nabla{\cdot {\sigma.}}}} & (2)\end{matrix}$

Here, ρ may denote the blood density, u may denote the blood velocity, tmay denote the time, and σ may denote the blood stress (which isdetermined by the blood pressure p and the blood viscosity). In someembodiments, an initial velocity of the model may be set to zero incomputation at the initial time phase. In the subsequent computations,the initial velocity of the model may not be set to zero, and grids inadjacent time phases may be matched. A computation result at a previousphase may be assigned to the corresponding grid at current time phase asan initial value.

The analysis result may include a physical state and acoefficient/parameter of any region or point of the model at currenttime phase. For example, a result of analyzing the coronary artery modelmay include a hemodynamics parameter at any region or point, such asblood velocity, blood pressure, wall stress of the blood vessel, wallshear stress (WSS) of the blood vessel, fractional flow reserve (FFR),coronary flow reserve (CFR), or the like, or any combination thereof. Asshown in FIG. 7, 718 may illustrate an analysis and computation ofhemodynamics.

In 616, it may be determined whether all of time phases have beencomputed. In response to the determination that all of time phases havebeen computed, 618 may be implemented. In response to the determinationthat not all of time phases have been traversed, 620 may be implemented.

In 618, the analysis result may be outputted. For example, the analysisresult may be sent to other modules or units of the blood flow conditionanalysis system 100. In some embodiments, the analysis result may bepost-processed. The post-processing may include generate a curve ortable of a relationship between the analysis result of the model andtime phase. According to the curve or table of the relationship, thepost-processing may further include outputting an estimated value of theanalysis result at any time phase. A process and result of thepost-processing may be found in FIG. 13 and its correspondingdescription. In some embodiments, the multi-time phase processing module440 may compare the generated multi-time phase computation result (e.g.,a vascular condition) and a reference result to generate a comparisonresult. As shown in FIG. 7, 716 may illustrate a result of thepost-processing. A process and result of the post-processing may befound in FIG. 13 and its corresponding description. In some embodiments,618 may further include comparing the generated multi-time phasecomputation result (e.g., a vascular condition) and a reference resultto generate a comparison result. The reference result may be stored inthe storage device 130 or the network 180, or input by a user. In someembodiments, the reference result and the related comparison result maybe stored in a table. For example, if the computation result is theblood velocity, the reference result may be a relationship between arange of the blood velocities and their corresponding risk. The degreeof risk may be divided into normal, warning, dangerous, extremelydangerous, etc. In some embodiments, the user may input the relationshipmanually based on clinical experiences. In some embodiments, thecomparison may be a comparison of the computation results of a sameobject at different time periods.

In 620, a model at a subsequent time phase may be matched with a modelat a current time phase. In some embodiments, a process of matchingmodels at different time phases may include: identifying characteristicregions of the models at different time phases; and correlating thecharacteristic regions corresponding to different time phases. In someembodiments, the characteristic region may be an entrance, a bifurcationregion, an exit, a stenosis region, a dilation region, etc. of the bloodflow. The 620 may include correlating the characteristic regionscorresponding to different time phases. In some embodiments, thecorrelation of the models corresponding to different time phases mayinclude registering the characteristic regions. As shown in FIG. 7, 706may be an embodiment of correlating a model and characteristic regionsof the model. In some embodiments, a characteristic region at differenttime phases may correspond to different numbers of grids. In someembodiments, the grids of the characteristic region at different timephases may be correlated by a specific algorithm or method. For example,if multiple grids at the first time phase correspond to a single grid orfewer grids at the second time phase, the matching unit 550 may averagethe values of the multiple grids at the first time phase, and thencorrelate the averaged values with the value(s) of the grid(s) at thesecond time phase. The corresponding grid values may include definingthe grid value at the first time phase as an input value of the gridcorresponding to the second time phase.

In 622, the subsequent time phase may be designated as the current timephase, and 614 may be implemented.

The above description of the present disclosure is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,modules may be combined in various ways, or connected with other modulesas sub-systems. Various variations and modifications may be conductedunder the teaching of the present disclosure. However, those variationsand modifications may not depart the spirit and scope of thisdisclosure. For example, 606 may be implemented before 604. A model ofthe entire region may be constructed based on image data, then a regionof interest may be selected and segmented from the model.

FIG. 8 is a flow chart illustrating a process for setting a boundarycondition according to some embodiments of the present disclosure. Insome embodiments, 610 may correspond to the process 800. In someembodiments, process 800 may be implemented by the parameter settingunit 520.

In 802, a model of a vascular region may be obtained. In someembodiments, the model may be obtained by 606. The model may be avascular model or a blood flow model. The vascular region may be aregion of coronary artery, abdominal artery, brain artery, lowerextremity artery, etc. In some embodiments, the vascular region may bepart or all of the region. For example, the vascular region may be anentire coronary artery model, a left coronary artery model, a rightcoronary artery model, or a coronary branch model (e.g., a left anteriordescending (LAD), a left circumflex (LCX), a diagonal branch, etc.). Insome embodiments, the model may be a vascular region in a mask form. Insome embodiments, the model may be a grid processed vascular model.

In 804, it may be determined whether the model of the vascular region isabnormal. The abnormal condition may include a vascular stenosis, athrombus, a vascular dilation, an angioma, etc. As shown in FIG. 9,model 910 may be a coronary artery model with two narrow regions 930 and940. The model 920 may be a normal coronary artery model, and regions(e.g., 935, 945) corresponding to 930, 940 are not narrow. In someembodiments, 804 may include extracting a centerline of the vascularregion model. In some embodiments, the vascular centerline may refer toan imaginary line located in the blood vessel along the trend of theblood vessel. The vascular centerline may include a set of one or morepixels (or voxels) in the blood vessel. In some embodiments, thevascular centerline may include a line of a set of one or more pixels(or voxels) in or near the center of the blood vessel. In someembodiments, the vascular center may include one or more vascularendpoints. The vascular centerline may be a path between the endpoints.In some embodiments, an exemplary method of extracting the vascularcenterline may refer to a PCT application No. PCT/CN2016/097294, filedon Aug. 30, 2016, an entire content of which are hereby incorporated. Aplurality of feature points may be predetermined along the vascularcenterline. A cross-sectional area of the model at the feature pointsmay be obtained. According to the area of the feature point, whether themodel is abnormal may be determined. For example, if there exists anabnormal reduction (for example, a feature point with a lowcross-sectional area locates between two feature points with normalcross-sectional areas) in the obtained cross-sectional areas of thefeature points, the model may be determined to have a stenosis orthrombus. In some embodiments, a number of the feature points may besufficient such that the change of the cross-sectional areas candetermine whether there is an abnormal condition. For example, thedistance between selected adjacent feature points is less than thelength of the narrow region. In response to the determination that themodel is normal, 822 may be performed. In response to the determinationthat the model is abnormal, 806 may be performed.

In 806, an abnormal region may be determined. For example, according toa change of cross-sectional areas of a model that is abnormal (also bereferred as an abnormal model), possible narrow or dilated regions maybe determined and marked. The narrow or dilated region may be a regionof a blood vessel where the local cross-sectional area is minimum ormaximum, or a region where the cross-sectional area changesdramatically. In some embodiments, the determined abnormal region may besent to the user. In response to the determination that the abnormalregion is not accurate, the user may modify the abnormal region. Forexample, the user may select manually one or more points or a range ofthe abnormal model as the abnormal region.

In 808, data related to the abnormal model may be obtained. The data mayinclude a blood velocity of the entrance of a vascular entrance, a bloodflow volume of the entrance, a blood pressure of the entrance, a flowresistance of the entrance, a number of branches, a number of entrances,a number of exits, a blood viscosity, a blood density, etc. of the bloodvessel. In some embodiments, the blood flow volume of the vascularentrance may be obtained based on a parameter or feature related to atissue or organ that connects to the blood vessel. For example, theblood flow volume of the coronary artery entrance may be estimated by acardiac output. The cardiac output is obtained by analyzing volumechanges of a heart chamber at a cardiac cycle. Some empiricalphysiological laws may also be employed to estimate the physicalquantities. For example, the blood flow volume of the coronary artery isproportional to the myocardial mass, i.e. Q∝Q_(o)M^(α), wherein Q maydenote the blood flow volume of the coronary artery, Q_(o) may denote aconstant, M may denote the myocardial mass, and the exponent α maydenote a predefined variation factor. In some embodiments, themyocardial mass M may be obtained by a noninvasive technique, such as bymultiplying a myocardial volume to a myocardial density. In someembodiments, the blood pressure of the coronary artery entrance may bemeasured by a blood-pressure meter, etc.

In 810, a normal model may be reconstructed based on the abnormal model.In some embodiments, only determined abnormal regions are reconstructed,and other regions are unchanged. In some embodiments, the methods ofreconstruction include lofting or stretching the blood vessel based ondiameters or centerlines to generate a normal region. In someembodiments, the reconstruction technique may include dilating ornarrowing the abnormal region. The dilated vascular cross-sectional areamay not be larger than vascular cross-sectional areas of adjacentregions. The narrowed vascular cross-sectional area may not be smallerthan a vascular cross-sectional areas of adjacent regions. In someembodiments, the adjacent regions of the reconstructed abnormal regionmay be smoothed to avoid significant mutations. In some embodiments, thereconstructed model may be sent to the user. In response to thedetermination that the reconstruction is not accurate, the user maymodify a part or all of the reconstructed normal model. For example, theuser may locally dilate, narrow, smooth the reconstructed normal model.

In 812, data corresponding to the normal model may be obtained. The datamay include a boundary condition, a flow resistance of each entrance andexit, etc. The boundary condition may include the blood pressure, thevelocity, the flow volume, etc. of the entrance and the exit. In someembodiments, the blood pressure and velocity of the entrance may beobtained in 808. The flow volume of the entrance may be obtained bycomputation. For example, assuming that the flow distribution of abranch vessel is positively correlated with the branch diameter, i.e.Q∝d^(k), wherein d denotes the average diameter of a proximal branchvessel (i.e., a region close to the branch), and k denotes anamplification factor. Then, the flow volume may be allocated in branchesof blood vessel in accordance with the positive relationship from theentrance, until the flow volume of the entrance is allocated to all thebranches. Based on the normal model and the boundary condition, acomputational fluid dynamics (CFD) simulation may be computed to obtaina flow resistance (i.e., a ratio of the entrance pressure to theentrance flow volume) at each entrance of the normal model.

In 816, a total flow resistance of the model may be obtained. The totalresistance may be computed by the following formula:

$\begin{matrix}{{R = \frac{P_{inlet}}{Q}},} & (3)\end{matrix}$wherein R denotes the total resistance of a model, P_(inlet) denotes theblood pressure intensity of an entrance, and Q denotes the flow volumeof the entrance. P_(inlet) and Q may be obtained in 808, and is notrepeated here.

In 818, the diameter of each proximal branch vessel at each level (e.g.a first level may represent branches at a first bifurcation, and asecond level may represent branches at a subsequent bifurcation of thefirst branches) may be determined by analyzing the vascular centerline,the vascular cross-sectional area, and the vascular abnormal region.

In 820, the flow resistance may be allocated based on the size of thediameter of the normal model. The flow resistance may be allocated basedon the following formula:R _(i) ^(j)=(d _(i) ^(−k)·Σ_(i) d _(i) ^(k))·R ^(j-1),  (4)wherein d denotes the diameter, i denotes the number of a bifurcationblood vessel at the current level, j denotes the level that the currentflow resistance assignment belongs to, and k denotes an allocationexponent of the flow resistance (for example, k of the coronary arterymay be set to 2.7).

In 822, a boundary resistance corresponding to an actual vascular modelmay be generated. In some embodiments, the boundary resistancecorresponding to an actual vascular model may be obtained based on aboundary resistance corresponding to the normal model. For example, theboundary resistance corresponding to an actual vascular model may be thesame as the boundary resistance corresponding to the normal model. Insome embodiments, the boundary resistance of the vascular model may beallocated based on a method described in 820.

FIG. 10 is a flow chart illustrating a process for a grid divisionaccording to some embodiments of the present disclosure. In someembodiments, process 1000 may correspond to 608. The process 1000 may beimplemented by the grid generation unit 540.

In 1002, a model may be obtained. The model may be described in someother embodiments of the present disclosure, such as a reconstructedmodel of a blood vessel/blood flow, a tissue/organ, or other region ofinterest of an object. As shown in FIG. 11, 1110 may be a coronaryartery blood flow model, e.g., model 1110 may represent the blood flowof the coronary vessel. Without considering conditions such as vesselwall thickness, vascular occlusion, etc., the model 1110 may alsoapproximately represent a coronary vascular model.

In 1004, a boundary region of the model may be obtained. If the modelcorresponds to a blood vessel or a blood flow related to the bloodvessel, the boundary region may be an exit, an entrance, a vascularwall, etc. As shown in FIG. 11, an entrance 1120 of the model 1110 maybe determined to be a boundary region of the model 1110 in 1004.

In 1006, surface grids of a determined boundary region may be generated(also referred to as a 2-dimensional grid division or 2D grid process).The surface grid division may include using grids to divide a surfacecorresponding to the boundary region. Algorithms used in the griddivision may include a triangular grid division, a quadrilateral griddivision, a hexagonal grid division, or the like, or a combinationthereof. Exemplary grid division algorithms may include a Loopalgorithm, a butterfly subdivision algorithm, a Catmull-Clark algorithm,a Doo-Sabin algorithm, a Delaunay triangular division algorithm, etc.Embodiments of the grid division technique may refer to FIG. 12 and itscorresponding description. As shown in FIG. 11, 1130 may becross-sectional diagram of an entrance of the model 1110, and 1140 maybe an exemplary result of the grid division 1130.

In 1008, surface grids of a side wall of the model may be generated. Insome embodiments, the side wall and boundary region may be divided bydifferent grid division techniques. For example, the side wall may bedivided by a surface grid subdivision algorithm. The surface gridsubdivision algorithm may include a mapping technique, an automatic gridgeneration technique, etc. The mapping technique may include: mappingthe side wall to a surface; dividing the surface by a 2-dimensional griddivision method; and mapping the divided grids to the side wall. Theautomatic grid generation technique may include: dividing the side wallinto multiple approximate surfaces according to the curvature ofdifferent regions of the side wall; and then implementing the2-dimensional grid division. The surface grid division may be foundelsewhere in the present disclosure, such as FIG. 12 and itscorresponding descriptions.

In 1010, volume grids of the model may be generated (also referred to asa 3-dimensional grid division or 3-D grid process) based on the resultsof the surface grid divisions of the boundary region and the side wall.The volume grid division may include dividing the model into3-dimensional grids. The 3-dimensional grids may include a tetrahedralgrid, a hexahedral mesh, a prismatic grid (i.e., a boundary layer grid),a mixture grid of tetrahedron and hexahedron, a Cartesian grid, a ballfilling grid, etc. In some embodiments, 1004 through 1008 may beomitted, e.g., the model may be directly divided by volume grids.

FIG. 12 illustrates a flow chart of a process for a grid divisionaccording to some embodiments of the present disclosure. In someembodiments, the process 1200 may be implemented by the multi-time phasefeature generation module 430. In some embodiments, 608 in the FIG. 6,1006 in FIG. 10, etc., may be implemented based on the process 1200.

In 1202, a 2-dimensional image may be obtained. In some embodiments, the2-dimensional image may be obtained by the data acquisition unit 510. Insome embodiments, the 2-dimensional image may be a 2-dimensional medicalimage, or an interesting region of the user (e.g., a coronary vascularregion, a brain region, etc.). Merely by way of example, the2-dimensional image may be a CT image, an MRI image, a PET image, or thelike. The 2-dimensional image may be presented in grayscale or color. Insome embodiments, the 2-dimensional image may be a 2-dimensionalpresentation of a model at a time phase. For example, the 2-dimensionalimage may be an image related to a boundary region of model in 1006.More particularly, the 2-dimensional image may display an entrance/exitregion of a vascular model (for example, as shown in FIG. 9). The2-dimensional image may be an image reconstructed by an image processingdevice (e.g., the processing device 120). The 2-dimensional image may bean image obtained from a local storage device or an external storagedevice (e.g., the storage device 130).

In 1204, the grid generation unit 540 may extract boundary points of aregion of interest of a 2-dimensional image. In some embodiments,extracting boundary points from the region of interest of the2-dimensional image may include segmenting the region of interest; andextracting the boundary points of the segmented region of interest. Atechnique of segmenting the region of interest may be found elsewhere inthe present disclosure. In some embodiments, the boundary points of theregion of interest may include one or more pixels in the boundary of theregion of interest (also referred to as “boundary pixels”). For example,the boundary points at the cross-section of a coronary artery mayinclude one or more boundary pixels located in the wall of coronaryartery. In some embodiments, the boundary pixels of the region ofinterest may be continuous, partially continuous, or discontinuous. Theterm “continuous” may refer to that a boundary pixel is adjacent to atleast one or more other boundary pixels. The extracted boundary pointsmay be stored in one or more storage devices (e.g., the storage device130, the storage module 260, etc.). The boundary points may be used bythe grid generation unit 540 or other unit/module with a data analysisfunction in subsequent processes. The exemplary i boundary point mayinclude the position of the boundary point, the number of the boundarypoint, or the like, or any combination thereof.

In 1206, one or more regions may be determined based on the boundarypoints. In some embodiments, the determination of the one or moreregions may be implemented by the grid generation unit 540. The one ormore regions may be formed by sequentially connecting the boundarypoints of the region of interest. Merely by way of example, thedetermination of one or more regions may include determining an initialboundary pixel of the region of interest (for example, a point with thesmallest x/y coordinates in the contour pixel may be selected as theinitial boundary pixel). The boundary pixels of the region of interestmay be sorted in a clockwise or counterclockwise direction. Startingfrom the initial boundary pixel, a previous boundary pixel may beconnected to a subsequent boundary pixel by a line to form a short edge.If the previous boundary pixel is connected to the initial boundarypixel, and a short edge is formed, a closed boundary curve may beformed. In some embodiments, the region of interest may be located in aclosed boundary curve. For example, the region of interest of the modelentrance 1140 in FIG. 11 (i.e., the region of grid division) may belocated in the boundary curve. In some embodiments, the region ofinterest may be a region located between two closed boundary curves. Forexample, the region of interest may be a 2-dimensional ring structure,or a structure equivalent to the 2-dimensional ring topology. Theinformation of one or more regions (e.g., a grid curve corresponding tothe region) may be stored in one or more storage devices (e.g., thestorage device 130, the storage module 260, etc.). The information ofone or more regions may be used by the grid generation unit 540 or otherunit/module with a data analysis function in subsequent processes.

In 1208, it may be determined whether a region needs a grid division. Insome embodiments, the determination may be implemented by thedetermination unit 580. In response to the determination that the regionneeds no grid division, the process 1200 may proceed to 1210. Inresponse to the determination that the region needs a grid division, theprocess 1200 may proceed to 1212. In some embodiments, a conditiondetermined by the determination unit 580 may include whether the regionis a region of interest. In response to the determination that theregion is a region of interest, the grid division may be determined tobe needed. As described elsewhere in this disclosure, the region ofinterest may include a region that needs a blood state analysis, e.g., aregion where blood flows in a specific blood vessel.

In 1210, the region that needs or does not need to be divided may bemarked. In some embodiments, the marking of the region may beimplemented by the grid generation unit 540. In some embodiments, themarking may be performed in a form of a computer readable code or anexecutable instruction. The marked region may be stored in one or morestorage devices (e.g., the storage device 130, the storage module 260,etc.). The marked region may be read by the grid generation unit 540 orother unit/module with a data analysis function in subsequent processes.For example, the marked region may be removed if a grid division isperformed.

In 1212, the region may be divided into grids. In some embodiments, thegrid division may be implemented by the grid generation unit 540. Insome embodiments, grid division may be performed based on the boundarypoints of the region. Algorithms used in the grid division may include atriangular grid division, a quadrilateral grid division, a hexagonalgrid division, or the like, or a combination thereof. Exemplary griddivision algorithm S may include a Loop algorithm, a butterflysubdivision algorithm, a Catmull-Clark algorithm, a Doo-Sabin algorithm,a Delaunay triangular division algorithm, etc. As another example, thegrid generation unit 540 may classify the boundary points of the regioninto different subsets, and sequentially grid-divide the boundary pointsof each subsets. The grid generation unit 540 may then combine the griddivision of the subsets to form a grid division of the region.Particularly, all of the boundary points of the region may be orderedaccording to the x/y coordinates (for example, the boundary points maybe firstly arranged in a non-descending order with respect to thex-coordinates, and then be arranged in a non-descending order withrespect to the y-coordinates for the points of the same x-coordinates).The ordered boundary points may be divided into a subset A and a subsetB based on their quantity. A Delaunay triangular division of the twosubsets may be completed respectively. Then the Delaunay triangulardivision of the subset A and the subset B may be combined to generate aDelaunay triangular division of all of the boundary points. In someembodiments, the grid division may also include superimposing theboundary curve of the region on the divided grids. In this case, theboundary curve of the region may be maintained in the divided grids(e.g., one or more short edges formed by the boundary pixels asdescribed in 1206).

In some embodiments, the grid division of a region may employ a gridgeneration technique based on parallel operations. For example, a regiondivision or similar algorithm may be employed to divide the region intomultiple sub-regions. Each of the sub-regions may be independentlydivided into grids. Then, the boundary grids of adjacent sub-regions maybe modified to obtain complete grids of the region.

In 1214, a grid division control condition may be set for the region. Insome embodiments, the setting of the grid division control condition maybe implemented by the grid generation unit 540. The grid divisioncontrol condition may control grid count, size, distribution, shape, orthe like, or one or more combinations thereof. In some embodiments, thegrid generation unit 540 may set an area constraint condition for a gridto limit the area of any grid in the region. For example, the gridgeneration unit 540 may set an area constraint value such that the areaof any grid is not larger than the area constraint value. In someembodiments, the grid generation unit 540 may set an interior angleconstraint condition for a grid such that the interior angle of any gridsatisfies the interior angle constraint condition. For example, the gridgeneration unit 540 may set an interior angle constraint value for atriangular grid such that the minimum internal angle of any triangulargrid is not less than the inner angle constraint value. In someembodiments, the grid division control condition may be obtained by auser via, for example, the communication device 140. The grid divisioncontrol condition may also be obtained by the grid generation unit 540or other unit/module with a data analysis function according to analysisof specific conditions. The specific conditions may include the timeneeded to generate the grids, the number of the generated grids, thetime of model computation based on the generated grids, an accuracydegree of the obtained result based on the generated grids, etc.

In 1216, whether the divided grid satisfies the control condition may bedetermined. In some embodiments, the determination of the grid divisionmay be implemented by the grid generation unit 540. In response to thedetermination that the divided grid does not satisfy the controlcondition, the process 1200 may proceed to 1218.

In 1218, the grid may be processed. In some embodiments, processing thegrid may be implemented by the grid generation unit 540. The gridprocessing may include one or more operations such as adjusting thenumber of grids, changing the size(s) of the grids, etc. Adjusting thenumber of grids may include increasing grid density, reducing griddensity, etc. Changing the size of the grid may include segmenting thegrid, merging the grid, reorganizing the grid, etc.

In some embodiments, if a triangular grid does not satisfy the areaconstraint condition (for example, the area of the triangular mesh cellis greater than the area constraint value), one or more auxiliary pointsmay be inserted in the triangular grid. The auxiliary points may beinserted randomly, or be inserted according to the position of thefeature points of the original triangular grid. The grid generation unit540 may generate a new grid based on the auxiliary points. For example,an auxiliary point may be inserted inside the triangular grid, e.g., tat the center of gravity of the triangular grid. Connecting theauxiliary point and vertices of the original triangular grid maygenerate three new triangular grids. As another example, a plurality ofauxiliary points may be inserted randomly or non-randomly in thetriangular gird cell. A Delaunay triangular grid may be divided byemploying the Delaunay triangular division algorithm according to themultiple auxiliary points. In some embodiments, if a triangular griddoes not satisfy an interior angle constraint, a specific algorithm maybe employed to process the triangular grid. For example, a flipalgorithm may be employed to update the triangular grid. Moreparticularly, the flip algorithm may include selecting a quadrilateralcontaining two adjacent triangular grids (i.e., a diagonal line of thequadrilateral is the adjacent edge of the two triangular grids);selecting another diagonal line as the adjacent edge of two newtriangular grids; and obtaining two new triangular grids. The innerangle constraint condition may include that the minimum interior angleof the triangular grid unit is not less than an inner angle constraintvalue. The inner angle constraint value may be 5°, 10°, 15°, 20°, 25°,etc.

The processed grid may return to 1216. The grid generation unit 540 maydetermine whether the processed grid satisfies a control condition.Until the grid satisfies the control condition, process 1200 may proceedto 1220.

In 1220, the grid generation unit 540 may determine whether all regionshave been analyzed. For example, the analysis of the regions may includedetermining whether the regions needs a grid division. In response tothe determination that not all regions have been analyzed, process 1200may return to 1208 to determine whether the remaining unanalyzed regionsneed to be grid divided. In response to the determination that allregions have been analyzed, grids of the interesting region may begenerated by the grid generation unit 540 in 1222. In some embodiments,algorithms that employed by grid division of different regions may bethe same or different. For example, all regions may employ the Delaunaytriangular division algorithm for grid division. For example, a part ofthe regions may employ the Delaunay triangular division algorithm forgrid division, and other part of regions may employ a quadrilateral gridalgorithm or a hexagonal grid algorithm for grid division. In someembodiments, the grid division control conditions of different regionsmay be the same or different. For example, the grid control conditionsof all regions may include area constraint conditions and/or interiorangle constraints. The area constraint controls and/or the interiorangle constraints of different areas may be the same or different. Moreparticularly, the interior angle constraint condition of all regions mayinclude that the minimum internal angle of any triangular grid is notless than an inner angle constraint value (e.g., 20°). As anotherexample, the area constraint condition of a brain image may include thatan area of the largest triangular grid is not greater than A, and thearea constraint condition of the vascular image may include that thearea of the largest triangular grid is not greater than B, wherein A issmaller than B.

The above description of the present disclosure is provided for purposesof illustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, modules maybe combined in various ways, or connected with other modules assub-systems. Various variations and modifications may be conducted underthe teaching of the present disclosure. However, those variations andmodifications may not depart the spirit and scope of this disclosure. Insome embodiments, 1210 may be omitted. In some embodiments, 1214 may beperformed before 1208, i.e., the grid generation unit 540 may set thesame grid division control conditions for all regions that needs a griddivision. In some embodiments, the process 1200 may divide a3-dimensional image into grids. For example, the grid division of the3-dimensional region may employ a fast Delaunay based sphere packingtechnique. The grid generation unit 540 may generate nodes of grids in a3-dimensional geometric region by filling based on the sphere packingtechnique. The nodes may be generated with appropriate density accordingto the geometric features and spatial relations of the geometric model.Then a 3-dimensional grid may be generated by employing the fastDelaunay insertion technique.

FIG. 13 illustrates a flow chart of a process for obtaining hemodynamicparameters corresponding to a point according to some embodiments of thepresent disclosure. In some embodiments, process 1300 may be implementedby the multi-time phase feature processing module 440. In someembodiments, 468 in FIG. 4B may be implemented based on the process1300.

In 1302, the multi-time phase feature processing module 440 may obtain amulti-time phase hemodynamic parameter. In some embodiments, themulti-time phase hemodynamic parameter may be related to 614 through 618in process 600. The hemodynamic parameter may represent the blood flowcondition of a vascular region, such as the vascular region of acoronary artery, an abdominal artery, a brain artery, a lower extremityartery, etc. The hemodynamic parameter may include blood velocity, bloodpressure, wall stress of the blood vessel, wall shear stress (WSS) ofthe blood vessel, fractional flow reserve (FFR), coronary flow reserve(CFR), or the like, or any combination thereof. In some embodiments, themulti-time phase hemodynamic parameter value may correspond to a bloodflow condition in a specific time period. For example, the hemodynamicparameter at different phases in a cardiac cycle may be obtained suchthat the blood flow condition in the cardiac cycle may be obtained.Number of the obtained time phases may be 3, 5, 8, 10, 15, etc.

In 1304, the multi-time phase feature processing module 440 maydetermine a point. The point may be an arbitrary point on the surface ofa vascular entrance/exit, or an arbitrary point on the vascular wall orinternal space of a blood vessel. In some embodiments, the point may bedetermined by a user via, for example, the communication device 140.

In 1306, the multi-time phase feature processing module 440 mayinterpolate the hemodynamic parameter curve of the point. In someembodiments, the hemodynamic parameter curve may represent a blood flowcondition within a cardiac cycle. The interpolation may include fittingthe multi-time phase hemodynamic parameters based on a function. Thefunction may be linear or non-linear. Suitable non-linear functions mayinclude a polynomial function, a logarithmic function, an exponentialfunction, or the like, or any combination thereof. For example,according to a multi-time phase FFR value of a point on the surface of acoronary entrance, an FFR curve of the point within a certain time rangemay be obtained. After obtaining the FFR fitting curve of the point in acardiac cycle, an FFR curve of the point at any time may further begenerated according to the periodicity of the heart beating.

In 1308, the multi-time phase feature processing module 440 may obtainthe value of a hemodynamic parameter (e.g., an FFR value) of a point ata time phase of interest based on the parameter curve. The time phase ofinterest may be different from the multiple time phases obtained in1302. In some embodiments, the selection of the time phase of interestmay be implemented by a user via, for example, the communication device140. In some embodiments, the multi-time phase feature processing module440 may process the hemodynamic parameter values of the point based onthe hemodynamic parameter curve. For example, an average hemodynamicparameter value (e.g., an average FFR value) may be obtained based onthe values of hemodynamic parameters (e.g., FFR values) during a periodof time.

The above description of the present disclosure is provided for purposesof illustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, modules maybe combined in various ways, or connected with other modules assub-systems. Various variations and modifications may be conducted underthe teaching of the present disclosure. However, those variations andmodifications may not depart the spirit and scope of this disclosure.For example, before simulating a curve of hemodynamic parameters of apoint, the multi-time phase feature processing module 440 may obtainextra hemodynamic parameters of the point. The extra hemodynamicparameters may be obtained by an interpolation method, or by a user viathe communication device 140.

FIG. 14 illustrates a schematic diagram of a process for obtaining ahemodynamic parameter corresponding to a point according to someembodiments of the present disclosure. Image 1402 may illustratemulti-time phase image data including a heart region and an abdominalregion. Image 1406 may illustrate a coronary artery and a constructedcoronary artery model corresponding to an image at the same time phase.Image 1408 may illustrate an FFR distribution of the coronary artery atdifferent time phases. Blood flow conditions at the different timephases may be obtained in the process 600. Image 1404 may illustratespecific clinical data of an object, including a curve of the aorticpressure varying with time, and a curve of the phase coronary blood flowvarying with time. Image 1410 may illustrate a curve of the FFR of theobject varying with time.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “engine,” “unit,” “component,” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution—e.g., an installation onan existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities of ingredients,properties such as molecular weight, reaction conditions, and so forth,used to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A system, comprising: a storage device including a set of instructions for analyzing blood flow conditions; and at least one processor in communication with the storage device, wherein when executing the set of instructions, the at least one processor is configured to cause the system to: obtain vascular images at multiple time phases, including a first vascular image at a first time phase and a second vascular image at a second time phase, wherein the vascular images at multiple time phases correspond to a same blood vessel or a part thereof; generate multiple vascular models, wherein the multiple vascular models correspond to the vascular images at multiple time phases; obtain, according to the multiple vascular models, multiple conditions of the blood vessel or the part thereof including a first vascular condition and a second vascular condition, wherein the first vascular condition corresponds to the first vascular image, and the second vascular condition corresponds to the second vascular image; determine, according to the multiple conditions of the blood vessel or the part thereof, a relationship between condition of the blood vessel or the part thereof and time phase; and obtain, according to the relationship, a third vascular condition of the blood vessel or the part thereof at a third time phase.
 2. The system of claim 1, wherein the blood vessel comprises at least one of a coronary artery, an abdominal artery, a cerebral artery, or a lower extremity artery.
 3. The system of claim 1, wherein the multiple conditions of the blood vessel or the part thereof comprises at least one of blood velocity, blood pressure, wall stress of the blood vessel, wall shear stress (WSS) of the blood vessel, fractional flow reserve (FFR), or coronary flow reserve (CFR).
 4. The system of claim 1, wherein the third vascular condition comprises an average fractional flow reserve (FFR).
 5. The system of claim 1, wherein the at least one processor is further configured to cause the system to: correlate the multiple vascular models; and determine the multiple conditions of blood vessels by employing a technique of computational fluid dynamics (CFD) according to the correlation.
 6. The system of claim 5, wherein to correlate the multiple vascular models, the at least one processor is further configured to cause the system to correlate at least two of the multiple vascular models at an entrance, a bifurcation segment, a stenosis segment, or an exit of the blood vessel or the part thereof.
 7. The system of claim 1, wherein the at least one processor is further configured to cause the system to: generate grids of the multiple vascular models respectively; and match the grids of the multiple vascular models.
 8. The system of claim 1, wherein the relationship between condition of the blood vessel or the part thereof and time phase is represented by a curve or in a table.
 9. The system of claim 1, wherein the vascular images at multiple time phases are cardiovascular images obtained in one or more cardiac cycles.
 10. The system of claim 1, wherein the first time phase corresponds to a systole in a cardiac cycle, the second time phase corresponds to a diastole in the cardiac cycle, and the third time phase corresponds to a time phase in the cardiac cycle.
 11. A method implemented on a computing device having at least one storage device storing a set of instructions for analyzing blood flow conditions, and at least one processor in communication with the at least one storage device, the method comprising: obtaining vascular images at multiple time phases, including a first vascular image at a first time phase and a second vascular image at a second time phase, wherein the vascular images at multiple time phases correspond to a same blood vessel or a part thereof; generating multiple vascular models, wherein the multiple vascular models correspond to the vascular images at multiple time phases; obtaining, according to the multiple vascular models, multiple conditions of the blood vessel or the part thereof, including a first vascular condition and a second vascular condition, wherein the first vascular condition corresponds to the first vascular image, and the second vascular condition corresponds to the second vascular image; determining, according to the multiple conditions of the blood vessel or the part thereof, a relationship between condition of the blood vessel or the part thereof and time phase; and obtaining, according to the relationship, a third vascular condition of the blood vessel or the part thereof.
 12. The method of claim 11, wherein the blood vessel comprises at least one of a coronary artery, an abdominal artery, a cerebral artery, or a lower extremity artery.
 13. The method of claim 11, wherein the multiple conditions of the blood vessel or the part thereof comprises at least one of blood velocity, blood pressure, wall stress of the blood vessel, wall shear stress (WSS) of the blood vessel, fractional flow reserve (FFR), or coronary flow reserve (CFR).
 14. The method of claim 11, wherein the third vascular condition comprises an average fractional flow reserve (FFR).
 15. The method of claim 11, further comprising: correlating the multiple vascular models; and determining the multiple conditions of blood vessels by employing a technique of computational fluid dynamics (CFD) according to the correlation.
 16. The method of claim 15, wherein the correlating the multiple vascular models comprises correlating at least two of the vascular models at an entrance, a bifurcation segment, a stenosis segment, or an exit of the blood vessel or the part thereof.
 17. The method of claim 11, further comprising: generating grids of the multiple vascular models respectively; and matching the grids of the multiple vascular models.
 18. The method of claim 11, wherein the relationship between the condition of the blood vessel or the part thereof and time phase is represented by a curve or in a table.
 19. The method of claim 11, wherein the vascular images at multiple time phases are cardiovascular images obtained in one or more cardiac cycles.
 20. A non-transitory computer readable medium, comprising executable instructions for analyzing blood flow conditions that, when executed by at least one processor of an electronic device, direct the at least one processor to perform actions of: obtaining vascular images at multiple time phases, including a first vascular image at a first time phase and a second vascular image at a second time phase, wherein the vascular images at multiple time phases correspond to a same blood vessel or a part thereof; generating multiple vascular models, wherein the multiple vascular models correspond to the vascular images at multiple time phases; obtaining, according to the multiple vascular models, multiple conditions of the blood vessel or the part thereof, including a first vascular condition and a second vascular condition, wherein the first vascular condition corresponds to the first vascular image, and the second vascular condition corresponds to the second vascular image; determining, according to the multiple conditions of the blood vessel or the part thereof, a relationship between condition of the blood vessel or the part thereof and time phase; and obtaining, according to the relationship, a third vascular condition of the blood vessel or the part thereof. 